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Identifying Coordinate Systems for Shapefiles

How to identify unknown coordinate systems for shapefiles

If you’ve ever worked with spatial data in GIS software, you’ve likely come across the challenge of dealing with shapefiles that lack a clearly defined coordinate system.

Coordinate systems are the foundation for understanding and working with geospatial data, as they provide a standardized way to represent geographic locations on the Earth’s surface. When a shapefile is missing its coordinate system, it can lead to misaligned layers, incorrect measurements, and a host of other issues that hinder accurate data visualization and analysis.

In this blog post, we’ll delve into the art and science of identifying coordinate systems for shapefiles with unknown projections!

When you have a shapefile with an unknown coordinate system, it’s important to identify the correct one to ensure proper data visualization and analysis.

Here’s a step-by-step process to help you identify the coordinate system:

Check metadata and accompanying files:

When working with a shapefile, it’s essential to check its metadata and accompanying files to gain insights into the dataset, such as its coordinate system, attribute information, and data sources. Here’s how to do it:

  1. Check accompanying files: A shapefile consists of several mandatory and optional files, including:
    • .shp: The main file containing geometry data.
    • shx: The index file for quick access to geometry data.
    • dbf: The dBASE table containing attribute data.
    • prj: The projection file containing the coordinate system information (if available)
    Look for any optional files, like .txt or .xml, that might contain metadata or additional information about the dataset.
  2. Examine the .prj file: The .prj file stores the projection and coordinate system information for a shapefile. If this file is missing or empty, you’ll need to identify the coordinate system yourself. If the file is present and populated, you can open it with a text editor to view the Well-Known Text (WKT) description of the coordinate system.
  3. Explore attribute data: Open the attribute table of the shapefile in your GIS software (e.g., QGIS, ArcGIS) to examine the attribute data. This may give you additional context about the dataset, including its source, collection methods, or other relevant information.
  4. Review any accompanying documentation: Sometimes, shapefiles come with accompanying documentation files, like a README.txt or a PDF report, that provide information about the dataset. Make sure to review these documents for any details about the coordinate system, data source, or other important information.
  5. Reach out to the data provider: If you cannot find the information you need in the metadata or accompanying files, consider reaching out to the data provider or source. They may be able to provide the necessary information, such as the coordinate system or data collection methods.

By thoroughly checking the metadata and accompanying files of a shapefile, you can gain valuable insights that will help ensure you’re working with the data correctly and producing accurate results in your geospatial analyses.

Look for clues in the data:

Looking for clues in the data by observing the range of x and y values can provide valuable hints about the coordinate system used for a shapefile. By understanding the nature of the values, you can often make an educated guess about the coordinate system being employed. Here are some common examples:

  1. Geographic Coordinate System (Decimal Degrees):If the range of x values is between -180 and 180, and the range of y values is between -90 and 90, the data is likely using a Geographic Coordinate System (GCS) with decimal degrees. GCS uses a 3D spherical model of the Earth’s surface, where coordinates are represented as latitude (y) and longitude (x) values.
  2. Projected Coordinate System (Meters or Feet):If the range of x and y values is much larger, typically in the thousands or tens of thousands, the data may be using a Projected Coordinate System (PCS). A PCS is a 2D representation of the Earth’s surface using linear units such as meters or feet. Examples of PCS include the Universal Transverse Mercator (UTM) system and regional State Plane Coordinate Systems (SPCS).
    • UTM Zones: The range of x values (easting) will typically be between 160,000 and 834,000 meters, while the range of y values (northing) will usually be between 0 and 10,000,000 meters.

Remember that these are only general guidelines and not definitive answers. When you’ve identified the likely coordinate system based on the x and y value ranges, it’s crucial to verify your assumptions by comparing your shapefile with another dataset that has a known coordinate system.

Compare with known data:

Comparing your shapefile with another dataset that has a known coordinate system is an effective way to verify or identify the coordinate system of your shapefile. By visually checking the alignment of the two datasets, you can determine whether they are using the same or compatible coordinate systems. Here’s a step-by-step guide to comparing your shapefile with known data using GIS software like QGIS or ArcGIS:

  1. Open your GIS software (QGIS, ArcGIS, etc.) and create a new project or map.
  2. Load your shapefile with the unknown coordinate system by using the “Add Vector Layer” or “Add Data” option, depending on your software.
  3. Add a reference layer with a known coordinate system. This layer should cover the same geographic area as your shapefile. Some common options for reference layers include:
    • Administrative boundaries (e.g., state or country borders)
    • Road networks
    • Satellite imagery or aerial photography (e.g., Google Maps, OpenStreetMap)
    • Another dataset from a reliable source (e.g., government agencies, research institutions)
  4. If the two datasets do not align correctly, try changing the coordinate system of your shapefile based on your initial assessment or best guess. You can typically do this by right-clicking on the layer and selecting “Set Layer CRS” (in QGIS) or by using the “Define Projection” tool (in ArcGIS).
  5. Continue testing different coordinate systems until you find one that results in a proper alignment between your shapefile and the reference layer.
  6. Once you’ve identified the correct coordinate system, make sure to update the .prj file or define the projection in your GIS software to avoid future issues.

Keep in mind that while comparing with known data can help you identify the correct coordinate system, it’s important to use a reference layer that is both accurate and relevant to your area of interest.

QGIS Find Projection Tool

The Find Projection Tool in QGIS is designed to create a shortlist of candidate coordinate reference systems (CRS) for a layer with an unknown projection, based on a specified target area. Here’s an outline of how this algorithm works:

  1. Input Parameters:
    • Layer with an unknown projection: The geospatial dataset (e.g., shapefile) with an unidentified coordinate system.
    • Target area: A geographic area in which the layer is expected to be located, defined by its bounding box (minimum and maximum latitude and longitude).
  2. Iterate through all known CRS:
    • For each CRS, project the layer’s extent (bounding box) into that CRS.
    • Check if the projected bounding box falls within or near the target area.
  3. Shortlist candidate CRS:
    • If the projected bounding box is close to the target area, add the CRS to the list of potential candidates.
    • Continue iterating through all CRS until the complete list of candidates has been created.
  4. Output:
    • The algorithm generates a shortlist of candidate CRS that, if used for the layer, would place it near the target area.
  5. Verification and Final Selection:
    • Manually test each candidate CRS using GIS software (e.g., QGIS, ArcGIS) by setting the CRS for the layer and comparing it with a reference layer with a known CRS.
    • Identify the correct CRS based on proper alignment and compatibility with the reference layer.

It’s essential to note that the algorithm might produce multiple candidates or no candidates at all, depending on the accuracy of the target area input and the extent of the layer. It’s also possible that the correct CRS might not be among the shortlisted candidates, so it’s crucial to verify the results and combine this approach with other methods to ensure a comprehensive assessment.

About the Author
I'm Daniel O'Donohue, the voice and creator behind The MapScaping Podcast ( A podcast for the geospatial community ). With a professional background as a geospatial specialist, I've spent years harnessing the power of spatial to unravel the complexities of our world, one layer at a time.

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